基于粒子群优化的最佳阈值法在局部放电信号去噪中应用

抑制白噪声干扰是局部放电（ Partial Discharge，PD）在线检测中的关键技术。提出一种基于粒子群优化的最优阈值选取去噪方法。该方法采用小波对局部放电信号进行分解，在选取阈值时建立广义交叉验证准则，以广义交叉验证准则作为适应度值函数，并结合粒子群优化算法自适应地确定出各分解层的最佳阈值。该方法不依赖任何先验知识，实现局部放电信号自适应去噪。对局部放电仿真信号和实测局部放电信号的去噪结果表明：本文提出的方法与标准阈值法相比，能更好地去除局部放电信号中的白噪声。

Application of Optimum Threshold Method Based on Particle Swarm Optimization in Partial Discharge Signal De-noising

The suppression of white noise interference is one of the key techniques of on-line monitoring of partial discharge. This paper proposes a de-noising method based on particle swarm optimization adaptive wavelet threshold estimation. The wavelet de-noising algorithm is based on an optimum and adaptive shrinkage scheme. When choosing the threshold, the generalized cross validation criterion is established and is used as fitness function. By using the particle swarm optimization algorithm, the optimum threshold of every decomposition scale is adaptively determined. The threshold selection method which does not rely on any prior knowledge is an adaptive method. The de-noising results of simulation signals and field PD signal show that compared with the standard threshold estimation method, the method proposed in this paper can remove the white noise in PD signals more effectively.